48440 research outputs found
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Reimagining Digital Education at Sri Sangwan School
No full textProviding inclusive education for students with unique needs and disabilities presents a significant challenge. Working with a special education school in Thailand, Sri Sangwan School, we examined current and potential assistive solutions to enhance the student's abilities when utilizing computers for learning. After conducting surveys, interviews, and ethnographies, our findings indicate that improving computer usage with assistive devices will significantly enhance the learning experience for the students. Technology skills continue to be a key interest for students, empowering them to excel in higher education. We recommended integrating specific assistive tools and infrastructure into their IT curriculum to empower students to achieve digital literacy skills and pursue higher education and careers
Attracting Tourists and Supporting Locals: Implementing Community-Based Tourism Strategies in the Amphawa Region
No full textIn recent years, the Amphawa region of Samut Songkhram, Thailand has experienced an overall decrease in tourism, especially in areas outside of the popular floating market. Additionally, the lack of communication of cultural information to visitors does not allow tourists to fully engage with and understand the qualities that make Amphawa a unique destination. To help address these issues, we were given the opportunity to work with the Municipality of Amphawa, which strives to support its community and enhance the struggling tourism industry throughout the region. Accordingly, this project aims to identify ways to promote tourism and local culture in Amphawa through community-based tourism practices. Through archival research, ethnography, surveys, and semi-structured interviews with primary stakeholders, we were able to develop recommendations and models of innovative solutions to achieve this goal
Stock Market Simulation 2507: Socially Responsible Investing
No full textThe goal of this project is to explore and compare the profitability of socially responsible investing (SRI) and representative emulation investing (REI). By comparing the two investment methods, it was hoped the participant would better understand how morality and social welfare play into the stock market. Two portfolios were created, one for each investment method. Each portfolio made 5 100,000.00, each. Both portfolios had some volatility over the four-week data collection period. The SRI portfolio lost 7,794.50. The REI portfolio increased its initial portfolio value by 8% while the SRI portfolio stayed basically the same despite some initial highs. The performance of these two portfolios indicated that companies that put all their focus on profit did better compared to those SRI companies. The experiences learned in this project will be useful in future investment
BluNew
No full textThis report explores the potential of the Blue Economy as a path toward sustainable economic revitalization in Venice, Italy. Amid growing ecological degradation, depopulation, and an overreliance on mass tourism, the city faces a critical need to redefine its economic future. Through a partnership with the Venetian organization SerenDPT and the EU-funded BLUNEW project, this study investigates how Blue Economy principles, centered on innovation, sustainability, and the regenerative use of aquatic resources, can be harnessed to support long-term economic resilience. The research combines literature reviews, policy analysis, and interviews with six startups operating in Venice’s Blue Economy. These case studies reveal key challenges such as bureaucratic barriers, funding constraints, and infrastructural gaps, as well as success factors like network-building, ESG incentives, and alignment with EU policy frameworks. Deliverables include multimedia training modules, a guide for future entrepreneurs, and a handbook for visiting students, all designed to promote environmental stewardship and support the development of a viable, community-centered maritime economy in Venice
AI and Impulse Responses of Musical Instruments and Analog Equipment IQP (E Term 2025)
No full textCurrently, there is existing research about the opinions of generative AI audios, including the opinions of musicians creating the music and listeners. However, there is much less literature about musician’s opinions on using AI to assist them in post-production as a tool similar to existing technology. In this project, I study the opinions of non-musicians, hobbyist musicians, and professional musicians about a possible augmentative AI model intended to manipulate the timbre of an audio. This theoretical “Timbre AI model” would be able to translate audio produced by one instrument to make it sound as though it were produced by another instrument, essentially transforming or “transferring” the timbre of the audio. Timbre transfer could allow for a single instrument player to be able to mimic an entire symphony’s music or create music that cannot currently exist due to the limits of some instruments. In this project, I surveyed 30 musicians (6 self-reported professionals, 24 hobbyists) and 30 non-musicians about their opinions about AI intervention in different stages of music composition and according to different perspectives on the role of AI. I create an “AI-Usefulness Score” to compare the different categories as well as musicians’ opinions segregated by prior experience with electronic or synthetic instruments or audio software. There is no difference between musicians’ and non-musicians’ opinions on the usefulness of a Timbre AI model, even with prior experience with electronic or synthetic instruments. Musicians appear to be willing to embrace new technology, but still believe that music composition should be a human-driven process
Quantum Games 4
No full textQuantum Games IQP works to build resources to support students and educators in learning about certain phenomena in quantum mechanics. In particular, it focuses on the maintenance of the quantumgamesorg.github.io website and creating games on it under the direction of Professor Aravind. The purpose of this project was to continue the work on the Quantum Games website that was initiated by an earlier IQP group and continued by a later group. Our team built on the work of the earlier teams by expanding and reorganizing the website and improving its functionality. The later parts of this report will describe the new contributions made by our team
Plasma Density and Electron Temperature Measurement in a Radio Frequency (RF) Cathode using a Double Langmuir Probe
No full textA Modular Test Unit – Radio Frequency (RF) Cathode (MTU-RFC) was designed and constructed to allow for reconfiguration, integration of a diagnostic probe, and use with different propellants. Using a radio frequency (RF) plasma source as an alternative to thermionic hollow cathodes allows for alternative propellants to be used in electric propulsion systems while mitigating common failure modes and performance degradation present in thermionic emitters. A compensated and uncompensated double Langmuir probe (DLP) has been designed to measure the plasma density and electron temperature in the RF cathode. A thorough investigation into radio frequency compensation has been conducted to determine a design that can accurately measure the plasma parameters of interest. The uncompensated double Langmuir probe has been tested and validated using a glow discharge as a baseline. Despite the prevailing theory that the intrinsic compensation of a floating DLP is sufficient in an RF environment, the need for additional compensation is demonstrated through the use of the uncompensated DLP in the RF plasma. Measurements conducted with the compensated DLP compared to the MTU-RFC global model indicate good agreement of the temperature and density. Based on preliminary experimental results, the design of the compensated double Langmuir probe will be utilized in further testing to ensure measurement accuracy
Improve Efficiency and Accuracy of Brain Injury Estimation Through Deep Learning and Mesoscale Finite Element Modeling
No full textConcussion, a mild form of traumatic brain injury (mTBI), is common in contact sports and often underreported due to its subtle, transient symptoms and athletes’ reluctance to self-report. Although blood-based biomarkers and advanced imaging techniques are emerging, concussion diagnosis still heavily relies on subjective reporting, highlighting the need for objective and biomechanically-informed tools. Finite element (FE) human head models have been instrumental in understanding concussion mechanisms by estimating brain tissue responses such as strain. However, challenges remain in simulation efficiency, anatomical resolution, and the generalizability of models across individuals with different brain morphologies. This dissertation addresses three critical gaps in concussion studies by developing and evaluating multiple computational solutions across both FE and deep learning (DL) domains. First, the stability of simulated brain strain under varying kinematic input filters was assessed. Despite differences in peak angular velocity introduced by filtering, peak maximum principal strain (MPS) and its spatial distribution were found to remain relatively robust, supporting the consistency of strain-based injury metrics across studies using different preprocessing methods. Second, a subject-specific convolutional neural network (CNN) was developed to account for individual brain size variations across three anatomical axes and achieved good prediction accuracy. However, training such models from scratch requires extensive data, often infeasible in practice. To address this, a method was developed to generate synthetic training data from limited real-world impact data. While synthetic data alone lacked the realism to fully train accurate CNNs, transfer learning using pretrained models significantly improved prediction accuracy, especially when real-world data were scarce. Third, the feasibility and added value of mesoscale FE modeling were explored. A 2D mesoscale model demonstrated improvements in strain distribution prediction over coarsely meshed global models. Building on this, a novel 3D mesoscale model was developed to capture detailed strain and axonal strain distributions at the brain’s gray and white matter interface—a region frequently implicated in concussion pathology. The mesoscale model revealed sharp strain transitions at material boundaries not fully aligned with anatomical divisions, suggesting potential mechanical vulnerabilities. Together, these studies contribute robust methodological advancements for improving understanding concussion brain injury biomechanics. They emphasize the value of anatomically informed modeling, the integration of synthetic data to overcome data scarcity in deep learning, and the potential of mesoscale models in uncovering localized injury mechanisms. This work lays the foundation for developing faster, individualized, and anatomically precise tools for concussion brain injury studies
Parametric Study of a Radio Frequency Plasma Cathode for Electric Propulsion
No full textThis dissertation performs a parametric study of the performance of a radio frequency cathode (RFC) as it would relate to an electric propulsion system such as an ion thruster or a Hall thruster. The RFC is being considered for use on flight-ready systems to enable a wider range of propellant options, such as H_2 O, CO_2, and N_2-O_2 mixtures, than existing thermionic hollow cathode technology. This would also enable in-situ replenishment of a thruster-neutralizer combination for missions that would require additional fuel. Typically, an RFC consists of a cylindrical dielectric discharge chamber, an antenna around the dielectric chamber, an orifice downstream, and a gas inlet upstream. For a system without a coupled thruster, an anode plate is used downstream of the orifice to simulate the positively-charge plume of a thruster. The performance of an RFC can be evaluated by the electron current extracted and the current extraction cost. The current extraction cost is a measure of the amount of power required to extract 1 A of current out of the RFC. Included in the cost is the anode bias voltage. The lower the current extraction cost, the more efficient the performance of the RFC. As a reference, a thermionic hollow cathode typically performs at a current extraction cost around 30 W/A and the minimum current extraction cost reported in this study was around 100 W/A. The study consists of two main components with the first being the experimental parametric study. A modular radio frequency plasma cathode has been designed to assess the sensitivity of performance to several geometric parameters, operating conditions, and propellants. The plasma cathode is designed to operate with argon and N_2-O_2 mixtures, specifically a 79% N_2, 21% O_2 mixture. Results of experiments with argon are reported as well as a limited set of experiments with the N_2-O_2 mixtures. The experiments using N_2-O_2 mixtures were limited due to the oxidation of molybdenum resulting in an inability to reignite the plasma once the initial plasma was extinguished. For argon, the minimum current extraction cost was close to 100 W/A, as mentioned earlier, and for the N_2-O_2 mixture the minimum current extraction cost was nearly 550 W/A. The second main component is the development of a global model. The global model was also developed to examine performance trends using these propellants. The modeling results also include performance trends for H_2 O propellant. The global modeling results are presented for various operating conditions and geometric configurations. Provided in the plots are the calculated electron density, electron temperature, neutral species temperature, electron current extracted, and current extraction cost. Strictly observing the trends for the model, the performance can be summarized and is the same regardless of the propellant. The electron current is higher for smaller discharge chamber lengths, larger orifice diameters, and lower flow rates. The current extraction cost will decrease for smaller discharge chamber lengths, larger orifice diameters, and lower flow rates. Results from the experiments are also presented comparing the ideal electron current extraction and current extraction cost calculated using the global model with measurements. Reasonable agreement seems to exist between the global model and the experimental results. Modeling results are presented with an uncertainty of the effective ion collection area since the ion collector did not sit perfectly flush with the quartz tube. Argon results show a possible optimal discharge chamber length. In terms of the current extraction cost, the 76.2 mm discharge chamber length seemed to perform better than the other discharge chamber lengths. In the case of the 63.5 mm and the 76.2 mm discharge chamber length, it appears that better current extraction performance occurred with the 2 mm orifice. For the 127 mm discharge chamber length with the sheeted ion collector and 2 mm orifice, it appears that an optimal flow rate may exist. N_2-O_2 results were limited, and further testing should be performed with the multipole ion collector or with a sheeted tungsten ion collector. Material incompatibility between the oxygen plasma and the molybdenum sheeted ion collector limited the amount of testing and data collected. Initial results suggest that a larger orifice size is needed to extract the electron current from the RFC, especially for molecular propellants. Further work is needed to improve the design of the RFC for use with molecular propellants
A NeuroIS-based UX Measurement Model of Cognitive Engagement
No full textIn an increasingly digitized world, understanding how users cognitively engage with information systems is critical for designing effective systems and crafting meaningful user experiences (UX). Traditional methods for measuring cognitive engagement rely on self-reported data that is subjective, static, and has been contextually limited to enjoyable activities. This dissertation introduces a novel, objective, and dynamic measurement model grounded in the fields of Information Systems (IS) and Human-Computer Interaction (HCI) literature. Specifically, the model leverages eye-tracking technology to assess two components of cognitive engagement: attention through gaze-based metrics and absorption through pupil-based metrics. This measurement model is validated through two case studies situated in high-stakes, emotionally demanding contexts that extend beyond hedonic use: 1. a web-based medical decision aid used by surrogate decision makers of patients with traumatic brain injury in neurosciences Intensive Care Units, and 2. a clinical decision support system for chronic pain management. Both studies employ a mixed-methods approach using traditional statistics and machine learning (ML) to test the effectiveness of the proposed model of cognitive engagement and the sensitivity of the metrics involved. The results show the effectiveness of the model and the sensitivity of the eye-tracking metrics in objectively detecting differences in cognitive engagement based on both differences in design and attentional bias caused by chronic pain. Beyond academic contributions, this dissertation emphasizes the ethical and practical implications of real-time engagement measurement, advancing the design of intelligent, human-centered systems in complex domains like healthcare